CD4+ effector memory T cells and PTPN22 as novel biomarkers for immune dysregulation in dilated cardiomyopathy

Background Recent advancements in multiomics approaches have enhanced our understanding of the molecular mechanisms underlying immune dysregulation in dilated cardiomyopathy (DCM), but the roles of specific immune cell subsets and their regulatory genes remain unclear. Purpose This study aimed to investigate immune cell imbalances and regulatory genes in DCM, identify potential diagnostic biomarkers, and explore new therapeutic targets. Methods We conducted a differential expression analysis on the GSE101585 dataset, including whole blood transcriptomic data from 8 DCM patients and 8 healthy controls, using DESeq2 (|log2FC| > 2, P < 0.05). Immune cell infiltration was quantified using five algorithms (MCPcounter, ssGSEA, EPIC, xCELL, and CIBERSORT), with dysregulated immune cell populations identified through comparative analyses. Flow cytometric analysis was performed on peripheral blood samples from 40 DCM patients and 40 healthy controls to validate immune cell infiltration imbalances. Core module genes were determined through weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) centrality analysis. Diagnostic feature genes (AUC > 0.7) were selected through cross-validation using LASSO regression, SVM-RFE, and the Boruta algorithm. Gene–immune cell interactions were analyzed using Pearson correlation (|r| > 0.6, FDR < 0.05), and potential targeted drugs were predicted using DSigDB with enrichment score thresholds (P < 0.001, FDR < 0.01). Results Multi-algorithm immune infiltration analyses showed an elevated proportion of CD4+ effector memory T cells (CD4+ TEM cells) in the GSE101585 DCM cohort compared to healthy controls, with significant effect sizes (xCELL: Cohen’s d = 1.1, P < 0.05; ssGSEA: Cohen’s d = 1.8, P < 0.01). Subsequent validation in an independent clinical cohort (40 DCM patients vs. 40 healthy controls) confirmed this finding (median [IQR]: 7.54% [4.57–18.85] vs. 3.96% [1.53–5.30], P = 0.001). Differential expression analysis revealed 1,783 genes, with WGCNA showing a strong correlation between the magenta module and CD4+ TEM cells (r = 0.66, P < 0.05). This module contained 104 genes, defining a core immune-regulatory gene set for further investigation. PPI network analysis identified 36 hub genes, and machine learning cross-validation yielded four diagnostic biomarkers: LRRTM4, PTPN22, FAM175B, and PROM2 (AUC > 0.7). Notably, PTPN22 was strongly positively correlated with CD4+ TEM cells (r = 0.66, r² = 0.441, P = 0.005), while FAM175B was negatively correlated with CD56dim NK cells (r = -0.74, r² = 0.543, P = 0.001). DSigDB analysis predicted 87 potential therapeutic drugs, with PTPN22 showing the broadest targeting potential (50 drugs). Conclusion This study elucidates a CD4+ TEM cell-centered immunoregulatory network in DCM, highlighting novel diagnostic biomarkers and druggable targets that could enhance precision immunomodulatory strategies in DCM management.